NHS R Conference

This is a subtitle

Dec 9, 2024

The conference

Some information

The Talks

Time Title Speaker
09:45 – 09:55 Welcome and Housekeeping Zoë Turner
09:55 – 10:20 RAPping my head against a databricks wall Louise Schreuders
10:20 – 10:35 Reducing mental health inequalities among the BAME residents of Herefordshire and Worcestershire communities Oluwatimilehin Olabamiyo
10:35 – 11:00 Simplifying development of AI applications integrated in EHRs with Health Chain Jennifer Jiang-Kells
11:00 – 11:15 Beyond the dashboard: R for value added insights Nicola Farthing
11:45 – 12:00 Reimagining NHS dashboards: an open-source approach with plotly-dash Jennifer Struthers
12:00 – 12:25 Using Machine Learning and secondary care activity data to identify risk of cancer earlier Scarlett Kynoch
12:25 – 12:40 The patient does not exist – generating synthetic patient data with Wasserstein GAN Simon Newey
12:40 – 13:05 Streamlining machine learning development at the NHS via open-source tools Elias Altrabsheh and James Sibbit
13:05 – 13:07 rainbowR Ella Kaye
13:55 – 14:10 What insights did Glasgow Scottish Ambulance Service (SAS) gain from combining multiple data sources about all chest pain patients from 2023? We'll present about the process and findings of a 1-year long MSc dissertation project. Katalin Koszegi
14:10 – 14:25 Predictive Modelling for health and social care capacity planning using open data Sebastian Fox
14:25 – 14:50 To explain or predict: how different modelling objectives change how you use the same tools Chris Mainey
14:50 – 15:05 Using Openxlsx2 to automate excel publications Ruth Keane
15:35 – 15:50 What I learnt about (programming) languages by building bilingual websites Rosemary Walmsley
15:50 – 16:15 Leveraging R to implement novel theoretical development in online ‘digital twin’ simulation modelling Richard Wood
16:15 – 16:30 Should I use your package Colin Gillespie
16:30 – 16:45 Cracking open the TiN: how we build a one-stop statistics website using R, GitHub and BigQuery Mohan Del
Time Title Speaker
09:45 – 09:55 Welcome and Housekeeping Zoë Turner
09:55 – 10:20 The Reusability Crisis in Healthcare Analytics Rhian Davies
10:20 – 10:35 Shift staffing via task load prediction Marcos Fabietti
10:35 – 10:50 Unleashing the power of pathway simulation Sammi Rosser
11:15 – 11:40 New generic tests for cancer – with R is a clinical scientists best friend Joe Shaw
11:40 – 11:55 Beyond automation: a shiny app to maximise analytical impact routine reporting narrative Laura Birks
11:55 – 12:10 Sharpening my Python skills through self-development of web scraping bank complaints data Kenneth Quan
12:10 – 12:25 GitHub as a team sport Matt Dray
13:10 – 13:25 Presenting fingertips in data in a more friendly format Rachel Brown
13:25 – 13:50 A method to apply temporal graph analysis on electronic patient record data to explore healthcare professional patient interaction intensity John Booth
13:50 – 14:05 Deploying a Shiny app with Docker in a Raspberry Pi Pablo León Ródenas
14:05 – 14:20 Estimating flexible hazard rates for C diff recurrence from electronic health records using the SplinHazard Regression package and other methods in R Elisabeth Dietz
14:20 – 14:45 Assessment of patient feedback using Natural Language Processing (NLP) and textual data analysis in R Ana Singh
15:15 – 15:40 Forged in the fire: agile project management lessons from the frontline Chris Beeley
15:40 – 15:55 Community Talk – Turing Way Sophia Batchelor
15:55 – 16:05 Community Talk -NHS.Pycom Alex Cheung
16:05 – 16:15 Closing talk NHS-R Community and raffle Zoë Turner

RAP

wRAPping your head around the idea

RAP stands for

Reproducible Analytical Pipeline

Aim: create reproducible code to streamline a repetitive process such as regular reports or statsitcal analysis.

wRAPping your head around the idea

Key ideas of RAP:

  • Automation
  • Modular, re-usable code
  • Transparency
  • Open Standards and Tools
  • Version Control

wRAPping your head around the idea

Key ideas of RAP:

  • Automation
  • Modular, re-usable code
  • Transparency
  • Open Standards and Tools
  • Version Control

RAPping your head around the idea

flowchart LR
  A[Data Store] --> B(SAS)
  B --> |Sometimes automated?| C(Word)
  B -.-> D(Excel?) -.-> C
  C --> E(PDF)
  F{Quality Assurance} --> B 
  F --> C
  E --> G[Published]

  subgraph Loop["Repeat every time the data / analysis changes"]
    B
    C
    D
    F
  end
  
  style Loop fill:none,stroke:#000,stroke-width:2px;

But why bother?

The Sudlow Review: ‘Uniting the UK’s Health Data: A Huge Opportunity for Society’
an independent review commissioned by the Chief Medical Officer for England, the UK National Statistician and NHS England’s National Director for Transformation.

Stakeholders’ highest priorities for improvements…in the health data ecosystem…
- Improve data usability: the implementation of open, shareable and reproducible approaches to data management, curation and analysis pipelines will drive transparency and efficiency, and reduce duplication of effort

But why bother?

Better, broader, safer: using health data for research and analysis
review into how the efficient and safe use of health data for research and analysis can benefit patients.

Summary recommendations
7. Promote and resource ‘Reproducible Analytical Pathways’ as the minimum standard for academic and NHS data analysis: this will produce high quality, shared, reviewable, re-usable, well-documented code for data curation and analysis; minimise inefficient duplication; avoid unverifiable ‘black box’ analyses; and make each new analysis faster.

Some reading